try:
int(s)
return True
except ValueError:
return False
def acquireID():
inputOk = False
k = 0
while inputOk!=True:
inp = raw_input(MSG_INPUT)
if isInt(inp):
k = int(inp)
if k>=0:
inputOk=True
else:
print MSG_ERRORE_ID_BASSO
else:
print MSG_ERRORE_NO_INT
return k
esci = False
while not esci:
path = fdb.getPath(acquireID())
if path!="":
esci = True
img = cv2.imread(path, cv2.IMREAD_COLOR)
cv2.imshow("pippo", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# FOR EVERY FOLD OF THE CROSS-VALIDATION PROCESS
for i in range(0,totIter):
# TRAINING
# The training set has the following structure:
# [[groupClass, [IDs]], ...]
# Example:
# [["ClassA", [4, 8, 24]], ["ClassB", [15, 29, 1]], ...]
trainingSet = fc.getTrainingSet(i)
for group in trainingSet:
groupClass = group[0] # class of the images in this group
for ID in group[1]:
imgPath = fdb.getPath(ID)
# retrieve the image using imgPath and use it to train the model,
# knowing that its class is groupClass
#TESTING
# The test set has the following structure:
# [IDs]
# Example:
# [5, 15, 21, ...]
testSet = fc.getTestSet(i)
rankingList = []
for ID in testSet:
imgPath = fdb.getImgPath(ID)
ranking = # image classification, see the next comment
rankingList.append(ranking)